The effect of short‐term exercise training and nitric oxide on the adaptation of femoral vascular conductance at the onset of contraction
Bibliographic record
Abstract
We tested the hypothesis that short‐term mild‐ (M) and heavy‐intensity (H) exercise training (ET) would speed the adaptation of femoral vascular conductance (FVC) at the onset of contraction by a nitric oxide (NO) dependent mechanism. Sprague‐Dawley rats (n=18) were randomized to sedentary (S), M (20m/min5% grade) or H (40m/min5% grade) ET groups and trained 5d/wkfor 4 wks with equal ET volume. Rats were anesthetised and instrumented for measurement of arterial blood pressure and femoral artery blood flow. FVC was calculated. The triceps surae muscle group was stimulated to contract at 30% and 60% of maximal contractile force (MCF) before and after NO synthase blockade (L‐NAME, 5mg/kg IV). FVC data were fit with a mono‐exponential model. The time constant (τ) for FVC was not different (p>;0.05) between groups at 30% (S = 18 ± 13s; M = 15 ± 5s; H = 13 ± 14s) or 60% MCF (S = 24 ± 17s; M = 16 ± 4s; H = 19 ± 4s). L‐NAME increased (p<0.05) τ in all groups at 30% MCF (S = 25 ± 12s; M = 21 ± 6s; H = 27 ± 13s), but did not alter (p>;0.05) τ at 60% MCF (S = 25 ± 9s; M = 22 ± 9s; H = 20 ± 3s). The primary findings from this study were that: 1) short‐term ET did not alter the adaptation of FVC at the onset of contraction; and 2) regardless of training status, NO contributed to the regulation of FVC kinetics at the onset of moderate‐intensity contraction (30% MCF), whereas NO was not required for vasodilation at the onset of heavy‐intensity contraction (60% MCF). NSERC, Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".